Robust Geodesic Regression

TL;DR

This paper introduces robust geodesic regression methods on Riemannian manifolds using M-type estimators, improving resistance to outliers and demonstrating superior empirical performance, especially in high-dimensional and neuroimaging data.

Contribution

It extends geodesic regression to incorporate robust M-type estimators, providing a new approach that is less sensitive to outliers and applicable to complex manifold data.

Findings

L1 estimator outperforms L2 and Huber on high-dimensional manifolds

Tukey biweight estimator is superior on compact high-dimensional manifolds

Numerical and neuroimaging data validate the robustness and effectiveness

Abstract

This paper studies robust regression for data on Riemannian manifolds. Geodesic regression is the generalization of linear regression to a setting with a manifold-valued dependent variable and one or more real-valued independent variables. The existing work on geodesic regression uses the sum-of-squared errors to find the solution, but as in the classical Euclidean case, the least-squares method is highly sensitive to outliers. In this paper, we use M-type estimators, including the $L_1$, Huber and Tukey biweight estimators, to perform robust geodesic regression, and describe how to calculate the tuning parameters for the latter two. We also show that, on compact symmetric spaces, all M-type estimators are maximum likelihood estimators, and argue for the overall superiority of the $L_1$ estimator over the $L_2$ and Huber estimators on high-dimensional manifolds and over the Tukey biweight estimator on compact high-dimensional manifolds. Results from numerical examples, including analysis of real neuroimaging data, demonstrate the promising empirical properties of the proposed approach.